SÃO PAULO STATE UNIVERSITY SCHOOL OF NATURAL SCIENCES AND ENGINEERING ILHA SOLTEIRA BELISA CRISTINA SAITO CHARACTERIZATION OF CORN INBRED LINES FOR DISEASE RESISTANCE Ilha Solteira 2017 Campus de Ilha Solteira POSTGRADUATE PROGRAM IN AGRONOMY BELISA CRISTINA SAITO CHARACTERIZATION OF CORN INBRED LINES FOR DISEASE RESISTANCE Doctoral dissertation presented to “São Paulo State University (UNESP), School of Natural Sciences and Engineering, Ilha Solteira” for obtaining the title of Doctor. Specialty: Production System. São Paulo State University (UNESP), João Antonio da Costa Andrade Advisor Major M. Goodman Co-Advisor Ilha Solteira 2017 DEDICATION To my parents Goro and Mirian. ACKNOWLEDGEMENTS To my family, my father Goro Saito, my mother Mirian Cristina Savelli Saito, my sister Delise Gabriela Saito and my nephew Caio Augusto Saito Demori, for all their love and affection. I couldn’t imagine being here without all of you. To my fiancée, Leandro Sanches Silva, for all his love, support, and help during this time, without your support probably I will not be here. To my advisor Professor João Antonio da Costa Andrade, for all teachings, supporting, patience and dedication. I learned a lot from you and will be always grateful for it! I also would like to thank to my co-advisor Professor Major M. Goodman, for having welcomed me during the exchange, for the teachings, patience and dedication. To São Paulo State University – UNESP, specially to the field technicians and friends of the Teaching, Research and Extension Farm of UNESP – Campus of Ilha Solteira (FEPE) in particular to Cícero Orgeda Queiroz, Irso Alves da Silva, Júlio Cezar Ambrósio de Menezes, Wesley Donizete Martins Taboas and José Raimundo de Melo, because they were willing to help me while conducting the experiments. In many moments of hard work, tiredness and heat. We work hard and have a lot of fun! Thank you! I would like to say thank to the bosses of FEPE, Manoel Fernando Rocha Bonfim, Cesar Henrique Alves Seleguin and Juliano Borges de Abreu, because they were always solicitous, patient and providing support. To North Carolina State University – NCSU, specially for the field technician Wayne Dillard, for received me during the exchange, for teaching me “the English red neck” and for being my fake grandfather in America, your friendship is very important for me. Also to the field technician Dale Dowden, for having welcomed me during the exchange, for teachings, patience and dedication. I am grateful to all friends I made during all my time in Ilha Solteira, specially to my classmates, Edjair Augusto dal Bem and Michelle Traete Sabundjian, because we share the arduous journey of the Postgraduate and all friendship we share since our master’s, we made this past 6 years great! To my friends Antônio Flávio Ferreira, Apolyana Lorrayne Souza, Juliana Cabral, Wanderléia Rodrigues and Érica Ribeiro, thanks for your friendship, I am grateful for all time together! To my dear friends and co-workers Naiara Scarabeli Zancanari, Rafaella Vargas Rossini, Giseli da Rocha Lima, Marielle Cândido Lopes, Rafael William Romo Trindade, Vitor Guerra Ferreira, Arthur Pereira da Silva and Udenys Cabral Mendes, we shared many moments of fun, sadness and a lot, but a lot of hard work. Your support was amazing!! Leonardo Queiroz Silva because we worked hard together in those past four years, by much learning, many moments in the cerrado, many laughs. Thanks for your support, this work is yours too! To Bruna Mendes de Oliveira, my adventure partner in the land of the “Uncle Sam”, thanks for your friendship! To the Coordination for the Improvement of Higher Education Personnel (CAPES), for the scholarship granted. To the member of the examining board Dr. José Branco de Miranda Filho, Dr. Herberte Pereira da Silva, Dr. Marco Eustáquio de Sá and Dr. Paulo Cezar Ceresini, thanks for all teachings and suggestions you made. Thanks to all who contributed directly or indirectly to the development of this work!! “Não há ensino sem pesquisa e pesquisa sem ensino. Esses que-fazeres se encontram um no corpo do outro. Enquanto ensino, continuo buscando, reprocurando. Ensino porque busco, porque indaguei, porque indago e me indago. Pesquiso para constatar, constatando, intervenho, intervindo, educo e me educo. Pesquiso para conhecer e o que ainda não conheço e comunicar ou anunciar a novidade.” Paulo Freire, 1996 RESUMO O milho é uma das culturas mais extensamente cultivadas em todo mundo. A incidência e a severidade de doenças têm aumentado significativamente nos últimos anos acarretando perdas no rendimento e afetando a qualidade dos grãos. Muitos trabalhos têm sido desenvolvidos na tentativa de identificar híbridos resistentes às principais doenças que acometem a cultura do milho, mas poucos são os relatos de estudos com linhagens. Dessa forma, o objetivo deste estudo foi: 1) identificar linhagens resistentes e susceptíveis com base na área abaixo da curva de progresso de doenças (AACPD) para os sintomas de ferrugem tropical (TR), ferrugem polissora (SR), cercosporiose (GLS), helmintosporiose (NLB), mancha marrom (PBS) e mancha branca (PLS); 2) identificar linhagens resistentes e suscetíveis com base nos parâmetros de adaptabilidade e estabilidade fenotípica para os sintomas de cercosporiose, helmintosporiose, mancha marrom e mancha branca; 3) identificar as melhores datas de semeadura, com a maior ocorrência das doenças, para fins de avaliação de linhagens e outros genótipos para resistência. Cinquenta linhagens, derivadas de populações com grãos flint e dent, foram avaliadas em blocos casualizados com três repetições, aos 45, 60, 75 e 90 dias após a semeadura em duas épocas, para medição da AACPD. Para a análise de adaptabilidade e estabilidade, 41 linhagens foram avaliadas em blocos casualizados com três repetições, 30 dias após o florescimento feminino, em onze épocas de semeadura, usando o método de análise de regressão. Foram atribuídas notas de 1, 2, 3, 4, 5, 6, 7, 8 e 9 correspondendo a 0, 1, 10, 20, 30, 40, 60, 80 e > 80% de área foliar com sintomas de doença. Para a AACPD, a análise de variância conjunta foi significativa para TR, SR, GLS e PLS e a interação linhagens x épocas foi significativa para ferrugem tropical e polissora. Para GLS e NLB as 41 linhagens foram classificadas como resistentes, sendo que as maiores severidades de doenças ocorreram nas semeaduras entre Junho e Setembro. As linhagens IVF1-3, IVF1-7, IVF1 -9, IVF1-10, IVF1 - 11, IVF1 -25, IVF1-230, IVD1-2, IVD1 -2-1, IVD1-3, IVD1-9, IVD1 -12, 2F, 3F, 6F, 9F, 10F, 4C, 2D e 7D foram classificadas como resistentes para as doenças estudadas, sendo indicadas para o desenvolvimento de sintéticos. Para a mancha marrom e mancha branca, as semeaduras de Abril, Junho, Julho e Agosto apresentaram maiores severidades de doenças. As linhagens IVD1-9, IVD1-10, 7D, 10D e 2F podem ser indicadas no desenvolvimento de sintéticos resistentes. Palavras-chave: Ferrugem. Cercosporiose. Helmintosporiose. Mancha branca. Mancha Marrom. ABSTRACT Corn is one of the most widely cultivated crops in the worldwide. The incidence and severity of diseases affecting crops have increased significantly in the past years, leading to yield losses and affecting grain quality. Many studies have been carried out with the attempt to identify hybrids that are resistant to the main diseases, but few reports have studied inbred lines. Therefore, the objectives of this study were: 1) identify resistant and susceptible inbred lines based on the area under disease progress curve (AUDPC) for tropical rust, southern rust, gray leaf spot, northern leaf blight, physoderma brown spot and phaeosphaeria leaf spot; 2) identify resistant and susceptible inbred lines based on adaptability and stability parameters for symptoms of gray leaf spot (GLS), northern leaf blight (NLB), physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS); 3) identify the best planting dates, with the highest occurrence of diseases, for the purpose of evaluating inbred lines and other genotypes for resistance. For AUDPC, fifty inbred lines, derived from populations with flint and dent grains, were evaluated in randomized block designs with three replications, at 45, 60, 75 and 90 days after planting in two seasons. For the analysis of adaptability and stability, forty-one inbred lines were evaluated in randomized blocks with three replications, 30 days after silking, in eleven planting dates, using regression analysis method. The scale of scores from 1, 2, 3, 4, 5, 6, 7, 8 and 9 corresponding to 0, 1, 10, 20, 30, 40, 60, 80 and > 80% of leaf area with disease symptoms was used. For AUDPC, the joint analysis of variance was significant for TR, SR, GLS and PLS, while the interaction inbred lines x environments, was significant for TR and SR. For GLS and NLB, forty-one inbred lines were classified as resistant and the highest severities of diseases occurred in planting dates between June and September. The inbred lines IVF1-3, IVF1-7, IVF1 -9, IVF1-10, IVF1 -11, IVF1 -25, IVF1-230, IVD1-2, IVD1 -2-1, IVD1- 3, IVD1-9, IVD1 -12, 2F, 3F, 6F, 9F, 10F, 4C, 2D and 7D were classified as resistant to the diseases studied and are indicated to produce synthetics. For PBS and PLS, the plating dates of April, June, July and August showed higher disease severity. The inbred lines IVD1-9, IVD1- 10, 7D,10D and 2F may be indicated to produce synthetics. Keywords: Rust. Gray leaf spot. Northern leaf blight. Physoderma brown spot. Phaeosphaeria leaf spot. LIST OF FIGURES Figure 1 - Diagrammatic scale for evaluating the incidence of foliar diseases in corn, according to Agroceres (1996). 21 Figure 2 - Temperature and relative humidity in Ilha Solteira – SP, Brazil from February to July 2014. 35 Figure 3 - Evolution of the scoring of the IVD1-3, IVF1-7 and IVF1-8 inbred lines for gray leaf spot in the first planting season, Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. 38 Figure 4 - Temperature, rain and humidity in Selvíria - MS, Brazil from October 2013 until December 2014. 47 Figure 5 - Temperature, rain and humidity in Selvíria - MS, Brazil from October 2013 until December 2014. 59 Figure 6 – Regression of severity for physoderma brown spot (PBS) symptoms as a function of environmental indices for the five corn inbred lines considered more resistant, evaluate in 11 environments (October and November 2013 and January until September 2014). Selvíria - MS, Brazil, 2014. 65 Figure 7 - Regression of severity for phaeosphaeria leaf spot (PLS) symptoms as a function of environmental indices for the two corn inbred lines considered more resistant, evaluate in 11 environments (October and November 2013 and January until September 2014). Selvíria - MS, Brazil, 2014. 68 LIST OF TABLES Table 1 - Joint analysis (mean squares) of Area Under the Disease Progress Curve (AUDPC) for tropical rust (TR), southern rust (SR), gray leaf spot (GLS), northern leaf blight (NLB), physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS). Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. 36 Table 2 - Individual analysis of variance for Area Under the Disease Progress Curve (AUDPC) for both planting dates (season 1: 02.20.2014 and season 2: 04.17.2014) to tropical rust, southern rust, gray leaf spot, northern leaf blight, physoderma brown spot and phaeosphaeria leaf spot. Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. 37 Table 3 - Averages of inbred lines in season 1 (planting date 02.20.2014) for Area Under the Disease Progress Curve (AUDPC, non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. 40 Table 3 - Averages of inbred lines in season 1 (planting date 02.20.2014) for Area Under the Disease Progress Curve (AUDPC, non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. 41 Table 4 - Averages of inbred line in season 2 (planting date 04.17.2014) for Area Under the Disease Progress Curve (AUDPC non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. 41 Table 4 - Averages of inbred line in season 2 (planting date 04.17.2014) for Area Under the Disease Progress Curve (AUDPC non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. 42 Table 5 - Summary of the joint variance analysis for Gray leaf spot score (GLS) and Northern leaf blight score (NLB), for 41 corn inbred lines in 11 environments. Selvíria – MS, Brazil, 2014. 49 Table 6 - Environmental indexes (Ij) and environmental averages of 41 corn inbred lines in 11 environments for gray leaf spot (GLS) and northern leaf blight (NLB). Selvíria – MS, Brazil, 2014. 50 Table 7 - Adaptability and stability parameters estimated using Eberhart and Russell (1966) method, for gray leaf spot and northern leaf spot for 41 corn inbred lines, in 11 environments. Selvíria – MS, Brazil, 2014. 52 Table 7 - Adaptability and stability parameters estimated using Eberhart and Russell (1966) method, for gray leaf spot and northern leaf spot for 41 corn inbred lines, in 11 environments. Selvíria – MS, Brazil, 2014. 53 Table 8 - Summary of the joint variance analysis for physoderma brown spot (PBS) and phaeosphaeria leaf spot score (PLS), for 41 corn inbred lines in 11 environments. Selvíria – MS, Brazil, 2014. 61 Table 9 - Environmental indexes (Ij) and environmental averages of 41 corn inbred lines in 11 environments for physoderma brown spot (PBS) and phaeosphaeria leaf spot score (PLS). Selvíria – MS, Brazil, 2014. 61 Table 10 - Adaptability and stability parameters estimated, using Eberhart and Russell (1966) method, for physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS) score for 41 corn inbred lines, in 11 environments. Selvíria – MS, Brazil, 2014. 63 Table 10 - Adaptability and stability parameters estimated, using Eberhart and Russell (1966) method, for physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS) score for 41 corn inbred lines, in 11 environments. Selvíria – MS, Brazil, 2014. 64 SUMMARY 1 INTRODUCTION 15 REFERENCES 16 2 LITERATURE REVIEW 17 2.1 Diseases in corn crop 17 2.1.1 Rust 17 2.1.2 Gray leaf spot 18 2.1.3 Northern leaf blight 18 2.1.4 Physoderma brown spot 19 2.1.5 Phaeosphaeria leaf spot 20 2.2 Diseases evaluation 20 2.2.1 Disease progress curves 22 2.3 Breeding for disease resistance 22 2.3.1 Interaction Genotypes x Environments 23 2.3.2 Adaptability and stability 24 2.3.2.1 Method proposed by Eberhart and Russell 24 REFERENCES 27 3 RESISTANCE OF CORN INBRED LINES TO FOLIAR DISEASES IN TWO PLANTING DATES 33 3.1 Introduction 33 3.2 Material and methods 34 3.3 Results and discussion 35 3.4 Conclusion 42 REFERENCES 43 4. ADAPTABILITY AND STABILITY OF CORN INBRED LINES FOR RESISTANCE TO GRAY LEAF SPOT AND NORTHERN LEAF BLIGHT 45 4.1 Introduction 45 4.2 Material and methods 46 4.3 Results and discussion 49 4.4 Conclusion 53 REFERENCES 54 5 ADAPTABILITY AND STABILITY FOR RESISTANCE TO PHYSODERMA BROWN SPOT AND PHAEOSPHAERIA LEAF SPOT IN CORN INBRED LINES 57 5.1 Introduction 57 5.2 Material and methods 58 5.3 Results and discussion 60 5.4 Conclusion 69 REFERENCES 70 6 CONCLUSION 72 7 FINAL CONSIDERATIONS 73 15 1 INTRODUCTION The corn (Zea mays L.) is a gramineae belonging to the Poaceae family, tribe Maydeae, diploid species (2n=20), monoecious and allogamous. It is cultivated worldwide between latitudes 58° North and 40° South, distributed in the most diverse altitudes, from locations below sea level until regions with more than 2,500 m of altitude (FANCELLI; DOURADO NETO, 2000). In Brazil, the crop is the second most extensively cultivated, being present in the whole national territory. It is estimated that for the 2016/17 season there will be an increase from 0.8% to 6.4% of the planted area, which in the previous year reached 5,387 thousand hectares. The estimated grain yield for the 2016/17 season will be approximately 83.1 millions tons of grain (COMPANHIA NACIONAL DE ABASTECIMENTO - CONAB, 2016). High losses in grain yield are associated with the incidence of diseases, several disease monitoring studies have been carried out by Brazilian Agricultural Reseach Corporation - Maize & Sorghum (EMBRAPA) and by the private sector. These studies have demonstrated that gray leaf spot, southern rust, tropical rust, common rust and corn stunt are among the main diseases of corn (CASELA et al., 2006). Due to the peculiar characteristics of the crop, such as the size of the plant, extension of the planting area and the economic yield, the use of genetic resistance is more viable for controlling the disease. Although chemical control is currently well accepted in high tech commercial crops, two decades ago it was viable only in seed production fields (GIANASI; CASTRO; SILVA, 1996). The most efficient strategy for disease control in corn is the identification and introduction of resistance genes, aiming at the production of resistant hybrids to most of the diseases that affect corn crop. With the development of corn inbred lines in Ilha Solteira – SP, it became interesting to check in greater detail the variation in resistance to disease among them. Thus, the objective of this study was to identify resistant and susceptible corn inbred lines based on the stability and adaptability parameters and Area Under the Disease Progress Curve (AUDPC) for disease symptoms of tropical rust (Physopella zeae (Mains) Cummins & Ramachar.), southern rust (Puccinia polysora Underw), gray leaf spot (Cercospora zeae-maydis Tehon & E.Y. Daniels), northern leaf blight (Exserohilum turcicum (Pass.) Leonard & Suggs), physoderma brown spot (Physoderma maydis) and phaeosphaeria leaf spot (Phaeosphaeria maydis in association with Pantoeae ananas). These inbred lines are promising for the production of resistant synthetics. 16 REFERENCES CASELA, C. R.; FERREIRA, A. S.; PINTO, N. F. J. A. Doenças na cultura do milho. Circular técnica 83: Embrapa. Sete Lagoas: Embrapa, 2006. v. 83. COMPANHIA NACIONAL DE ABASTECIMENTO - CONAB. Acompanhamento da safra brasileira: grãos. Observatório Agrícola, Brasília, DF, v. 2, n. 4, p. 1–60, 2016. FANCELLI, A. L.; DOURADO NETO, D. Produção de milho. Guaiba: Agropecuária, 2000. 360 p. GIANASI, L.; CASTRO, H. A.; SILVA, H.P. Raças fisiológicas de Exserohilum turcicum identificados em regiões produtoras de milho no Brasil, safra 93/94. Summa Phytopathologica, Botucatu, v. 22, p. 214-217, 1996. 17 2 LITERATURE REVIEW 2.1 Diseases in corn crop There are several diseases that affect the corn crop and they are responsible for reducing the grain yield and reducing the quality of grains and seeds. Losses due to diseases vary from year to year and their occurrence is strongly influenced by the environment. Some diseases may occur generally but do not cause much damage, while others may be potentially more harmful, depending on the disease, season and susceptibility of the genotypes (ROBERTSON et al., 2008). 2.1.1 Rust According to Pinho et al. (1999), rust is one of the diseases that affect corn crop since these were observed in most of producing regions, causing crop limitation, such as direct damage to the plant by reduction of the photosynthesizing area, which may lead to a reduction in grain yield of the crop. Rust is caused by fungi and the name of the disease is related to the ferruginous aspect presented by the mass of spores present in the central region of the pustules (REIS; CASA, 1996). In the corn crop, there are common rust, caused by Puccinia sorghi Schw, southern rust, caused by Puccinia polysora Underw, and tropical rust, caused by Physopella zeae (Mains) Cummins & Ramachar. Rust can cause substantial losses in grain yield in corn crop (CHEN et al., 2004). Costa et al. (2010) reported that in 2009/2010 crop, southern rust was responsible for severe epidemics in many corn producing regions in the states of Paraná, Santa Catarina and Rio Grande do Sul, requiring the application of fungicides for their control. For Reis, Casa and Bresolin (2004) the losses data from common rust have not yet been quantified in isolation. As for tropical rust, there are also no reports in the literature of the economic impact caused by the incidence of this fungus in corn. Many authors suggest that the use of hybrids or varieties with satisfactory levels of resistance to the pathogen as being the most efficient and least expensive control method to rust (MCGEE, 1988; PINTO; FERNANDES; OLIVEIRA, 1997; SHERF; MACNAB, 1986). 18 2.1.2 Gray leaf spot The etiological agent of gray leaf spot is Cercospora zeae-maydis Tehon & E.Y. Daniels. This is the most important disease in corn crop (BRITO et al., 2007). In Brazil, the disease was first reported in 1953 (CHUPP, 1953), in São Paulo. It was reported for the first time in the 2000/2001 crop and since then has been occurring generically, causing significant reductions in corn crop (FANTIN, 2004). In other corn-producing states, gray leaf spot has also caused significant damage (FANTIN et al., 2001; PINTO; ANGELIS; HABE, 2004; REIS; SANTOS; BLUM, 2007).The symptoms of gray leaf spot appear first in the lower leaves around 2 or 3 weeks before tasseling. The lesions are rectangular in shape and are delimited in width by the main nerves of the leaf. The lesions present brown coloration and in high humidity conditions (90%), with temperatures ranging from moderate to high (22 a 32 °C) and cold nights with dew, dense sporulation occurs, making the leaves gray, characteristic of this disease (CASELA; FERREIRA, 2003; ROBERTSON et al. 2008). According to Brito et al. (2007), the pathogen colonizes a large part of the leaf tissue, reducing the photosynthetic area, leading to early senescence and, consequently, to the reduction of grain yield. The same authors evaluated 12 commercial corn hybrids on the incidence of gray leaf spot, evidenced that the level of damage caused by the pathogen varies between planting dates and hybrids, that the reduction in grain yield mainly related to late planting date and that the use of resistant hybrids does not require chemical control of the disease. Munkvold et al. (2001), also suggest that the main strategy of control of gray leaf spot is the use of resistant hybrids. 2.1.3 Northern leaf blight According to Reis and Casa (1996), three similar diseases are described in corn crop in Brazil: northern leaf blight, southern leaf blight and helminthosporium leaf spot. Northern leaf blights, the most frequent, is caused by Exserohilum turcicum (Pass.) Leonard & Suggs (sin. Helminthosporium turcicum Pass.). It is distributed in most of crop producing regions of Brazil, constituting one of the main phytosanitary problems of this crop, with losses in grain yield reaching 60% in susceptible genotypes (RAYMUNDO; HOOKER, 1981). Symptoms of the disease appear approximately one week after the onset of the infection, characterized by elliptical lesions of straw staining that measure from 2.5 to 15 cm in length, with well-defined edges that become dark because of the fruiting of the fungus (WORDELL FILHO; CASA, 19 2012). The development of northern leaf blight is favored by the temperature between 18º and 27ºC, with an optimum temperature of 20ºC and the presence of dew on the surface of the leaves (SABATO et al., 2013). In Brazil, the disease occurs in greater intensity in the second crop causing the greatest damage when infecting plants during the flowering period. For Fernandes and Oliveira (2000) the development of E. turcicum is negatively correlated with photoperiod, light intensity and sugar concentration in corn plants. These conditions are more frequently observed in the second crop, which could explain the greater severity of this pathogen at that time. Many authors described the mechanisms of genetic inheritance associated with northern leaf blight. The disease is mainly controlled by the use of resistant cultivars through quantitative (non-specific) and qualitative (race-specific) resistance. Qualitative and quantitative resistance sources have been described, however, qualitative resistance is easily breakable in the presence of a virulent lineage (WELZ; GEIGER, 2000). The quantitative resistance confers partial resistance, in the case of northern leaf blight, causing a reduction in the development of the disease and the percentage of affected leaf area that can result in the expression of several components, including the incubation period, latent period, sporulation intensity size, number and rate of lesion growth (CARSON; GOODMAN, 2006; HURNI et al., 2015; PARLEVLIET, 2002). 2.1.4 Physoderma brown spot Physoderma brown spot is caused by the fungus Physoderma maydis, commonly occurring in regions with high temperatures and high precipitations, the first symptoms of the disease usually appear on leaf limbs and nerves with chlorotic spots (LEÓN, 1984). According to Robertson et al. (2008), the pathogen is dormant in infected tissues or soil and produces innumerable zoospores in the presence of water. Leaf infection occurs at whorl when water is present for an extended period of time, occurs in a day cycle and requires a combination of light, free water and temperature between 23.8 and 29.4ºC. According to Fernandes and Balmer (1990), the brown spot is more severe in late plantings, carried out in low areas. There are few reports in the literature regarding the quantification of this disease in corn worldwide. 20 2.1.5 Phaeosphaeria leaf spot Phaeosphaeria leaf spot, whose etiological agent is Phaeosphaeria maydis (PINTO; FERNANDES; OLIVEIRA, 1997) in association with the bacteria Pantoeae ananas (PACCOLA-MEIRELLES et al., 2001), is a disease that affects the major corn producing regions in Brazil and worldwide. Many researchers have argued that the disease is caused only by a fungus (Phaeosphaeria maydis), or only by the bacteria (Pantoeae ananas). However, evidence suggests that the symptoms are related to the joint action of both the fungus and the bacteria. The incidence of phaeosphaeria leaf spot increased significantly since 1990, causing damage mainly when planting occurs in rainy periods and mild temperatures. Losses are grain yields associated with this disease may reach 60% (WORDELL FILHO; CASA, 2012). The symptoms of this disease are related to the appearance of irregular green, dark-green leaf spots that appear on the lower leaves, passing to the higher leaves of the plant. Subsequently, the lesions become necrotic of straw coloration being able to coalesce. The symptoms may present in different severities depending on the corn genotype (PACCOLA-MEIRELLES et al., 2002; REIS; CASA; BRESOLIN, 2004). Sawazaki et al. (1997) suggest that under conditions of frequent and well-distributed rains, the pathogen can cause greater severity and drastically affect the grain yield. Lopes et al. (2007) studied the control of resistance of phaeosphaeria leaf spot from the evaluation of the means of the generations originating from the crossing between two resistant and one susceptible inbred line. These authors concluded that the additive gene effects predominate in the resistance to the phaeosphaeria leaf spot and that the characteristic has high heritability, which facilitates the genetic improvement. 2.2 Diseases evaluation The quantification of plant diseases, known as plant pathophysiology, is necessary for the study of disease control measures, the determination of fungicide efficiency or the characterization of varietal resistance, as well as for epidemiology, in the construction of diseases progress curves and in estimating the damage caused by it (AMORIM, 1995). The most common terms used in pathophysiology are incidence and severity, terms that are often misused. Incidence refers to the percentage (frequency) of diseased plants or parts of diseased plants in a sample or population. Severity is the percentage of area or volume of tissue 21 covered by symptoms (BERGAMIN FILHO; AMORIM, 1996). The severity parameter is the most appropriate to quantify foliar diseases in corn, the percentage of tissue area covered by symptoms represent the intensity of the disease better than the incidence. Several methods are described in the literature regarding the quantification of diseases in corn, with more frequent use of diagrammatic scales or scale of notes (AMORIM, 1995). The notes can be attributed to the entire crop, to an experimental plot or to an individualized plant. The best time to determine the resistance should be made at the phenological stages R3 or R4 (SABATO; PINTO; FERNANDES, 2013). Diagrammatic scales are illustrated representations of a series of plants,parts of plants with symptoms at different levels of severity (Figure 1). These scales are the main tool for assessing the severity of many diseases (BERGAMIN FILHO; AMORIM, 1996). Figure 1 - Diagrammatic scale for evaluating the incidence of foliar diseases in corn, according to Agroceres (1996). ——————————————— Affected leaf area (%) ———————————— 0 1 10 20 30 40 60 80 > 80 ——————————————————— Ratings —————————————— 1 2 3 4 5 6 7 8 9 ————————————————— Type of reaction ———————————— HR R R MR MR/MS MS S S HS HR – highly resistant. R – resistant. MR – moderately resistant. MR/MS – moderately resistant/moderately susceptible. S – susceptible. HS – highly susceptible. Source: Agroceres (1996). 22 2.2.1 Disease progress curves The disease progress curve is the best way to represent an epidemy, and it is usually expressed by disease versus time proportion. With the use of disease progress curves, it is possible to characterize the interactions between pathogen, host and environment, to define control strategies and to predict future levels of disease (BERGAMIN FILHO; AMORIM, 1996). Disease progress curves can be constructed for any pathosystem, with the important parameters being the time of onset of the epidemy, the initial inoculum amount (x0), the rate of disease increase (r), the shape of the progress curve of the disease, the area under this curve (AUDPC), the maximum (xmax) and final (xf) amounts of disease and the duration of the epidemy (BERGAMIN FILHO, 1995). The use of mathematical models to analyze disease behavior or progression over time is an important tool for phytopathologists. There are six mathematical models used for this purpose: the exponential model, logistic model, Gompertz model, monomolecular model, Richards model and the time-dependent model (BERGAMIN FILHO, 1995). 2.3 Breeding for disease resistance Several studies report that large yield losses of corn are associated with the incidence of diseases (BRANDÃO et al., 2003; JULIATTI et al., 2004; SANTOS et al., 2011). The incidence and severity of the diseases that affect the corn crop have been attributed mainly to the planting of corn in the straw without the adoption of crop rotation and also due to crop overlap. The incidence and severity of the diseases depend on susceptibility of genotypes, the concentration of inoculum, the race or aggressiveness of the pathogen and favorable environmental conditions, provided by the climate, soil, cropping system or inadequate crop management. Under favorable conditions and susceptible genotypes, different diseases can occur in high severity (PINTO; OLIVEIRA; FERNANDES, 2007). The most efficient control of diseases of the corn crop is the identification and introduction of resistance genes, aiming at the obtention of resistant hybrids and varieties. Many studies have been developed aiming at the identification and control of resistance mechanisms to many diseases (CHUNG et al., 2011; JINES et al., 2007; ZHANG et al., 2012). For Brito et al. (2012), one of the main causes of instability in the use of commercial hybrids in Brazil is the high disease severity, due to variations in the pathogen population, mainly caused by the planting of susceptible hybrids and changes in the production system. 23 Although the use of resistant cultivars is the most efficient and economical strategy for disease control, Yorinori and Kiihl (2001) consider that, for most diseases, the degree of resistance is insufficient to avoid losses to the level of economic damage and requiring the adoption complementary measures for its control. For diseases in which there is a source of resistance, this may be ephemeral, since the pathogen can develop new races or biotypes, before a new resistance gene. These same authors also suggest that it is fundamental to adopt integrated control strategies, where genetic resistance is one of the elements of the set of measures to be taken for maximum productivity, stability and profitability. In this context, the quantitative resistance, conferred by numerous loci of small effect, is also interesting, since it is more stable. 2.3.1 Interaction Genotypes x Environments The interaction between genotypes x environments (G x E), can be defined as a change in the relative performance of one trait, of two or more genotypes, measured in two or more environments. This is because the effect of the environment, almost always, is different in each of the genotypes. Interaction may, therefore, cause changes in the order of classification of genotypes in each environment and changes in the absolute and relative magnitude of genetic, environmental and phenotypic variances between environments. (BOWMAN, 1972). This fact demands that the breeding is carried out under the conditions in which the genotype will be used. This interaction is characterized when the behavior of the races, inbred lines or cultivars are not consistent in the different environments, that is, the response of each genotype is specific and different from other genotypes to the changes that occur in the environments (RAMALHO et al., 2012). The effect of the interaction GxE describes the differential behavior of the genotypes in the contrasting environments (COIMBRA et al., 2009). It is important to evaluate the magnitudes of the interactions GxE, since this knowledge guides the planning and the strategies of the improvement in the recommendation of cultivars, besides being determined in the phenotypic stability of cultivars, for a determined region (VENCOVSKY; BARRIGA, 1992). The use of phenotypic stability in the selection, in the early stages of breeding, is still rare, but it can be implemented in some cases where it is possible to evaluate a reasonable number of genotypes in several environments, which may even include different planting dates. For resistance to diseases this is still more valid because the potential of inoculum and conditions favorable to the various diseases vary throughout the year. The analysis of the interaction GxE is important for breeding programs, because it provides the base for selection to broad or specific adaptation, to choose selection 24 environments, to identify the level of stress in the selected environments and to indicate satisfactory levels of resistance (FOX; CROSSA; ROMAGOSA, 1997). Thus, the identification of genotypes with high adaptability and phenotypic stability is the most advantageous way to explore the interaction GxE (PEREIRA et al., 2009). 2.3.2 Adaptability and stability Many authors describe the concepts of phenotypic adaptation and stability, as well evaluation methods. Mariotii et al. (1976) describe the adaptive term as a potential capacity to develop environmental performance assessment systems, and stability is considered as an ability to generate performance data in environmental assessment systems. Chaves (2001) suggest that most methods of adaptability and stability analysis, using regression techniques, measure the variation of the quantitative character in relation to an environmental index. The methods differ according to the type of regression model used and the way of determining the environmental index. According to Cruz (2006), many methods have been proposed for the analysis of adaptability and stability, aiming to evaluate genotypes in different environments. These methods are based on G x E interaction and are distinguished from the concepts of stability and adaptability adopted and certain statistical principles selected. Among these are the methods based on analysis of variance (YATES; COCHRAN, 1938; PLAISTED; PETERSON, 1959; WRICKE, 1965; ANNICCHIARICO, 1992), linear regression (FINLAY; WILKINSON, 1963; EBERHART; RUSSELL, 1966; TAI, 1971), bissegmented regression (VERMA; CHAHAL; MURTY, 1978; SILVA; BARRETO, 1985; CRUZ; TORRES; VENCOVSKY, 1989), nonparametric analysis (HUEHN; NASSAR, 1990; LIN; BINNS, 1988), factor analysis (MURAKAMI; CRUZ, 2004) and main components (centroid and AMMI). The choice of the adaptability and stability analysis method depends on the experimental data, mainly related to the number of environments available, the precision required and the type of information desired (CRUZ; REGAZZI; CARNEIRO, 2012). The analysis of adaptability and stability widely used by corn breeders is the methodology proposed by Eberhart and Russell (1966). 2.3.2.1 Method proposed by Eberhart and Russell Finlay and Wilkinson (1963) proposed a methodology to evaluate the genotype 25 performance for each genotype, adjusting a simple linear regression of the dependent variable in relation to the environmental index. Eberhart and Russell (1966) expanded the model proposed by Finlay and Wilkinson (1963), in that both the regression coefficients of phenotypic values of each genotype in relation to the environmental index and deviations from that regression would provide parameter estimates of stability and adaptability (CRUZ; REGAZZI; CARNEIRO, 2012). Eberhart and Russell (1966) proposed a method of adaptability and stability study based on regression analysis. The parameters that express the adaptability and stability are the average, the linear response to the environmental variation and the deviation of the regression of each genotype, obtained from the model: 𝑌𝑖𝑗 = 𝛽𝑜𝑖 + 𝛽1𝑖𝐼𝑗 + 𝛿𝑖𝑗 + 휀𝑖𝑗 (1) Where: βoi: Overall average of genotype i (i = 1, 2, .., g); β1i: Linear response of genotype i to environmental variation; Ij: Environmental index (j = 1, 2, ..., a), being Ij = 𝑌.𝑗 𝑔 − 𝑌.. 𝑔𝑎 ; δij: Regression deviation; Ɛij: Average experimental error. According to this method, for disease symptoms analysis an ideal cultivar is one with an overall mean (βo) around 1, a linear regression coefficient (β1) lower than 1 and a variance of the regressions deviations (σ2 di) equal to zero. Such a value of β1<1 indicates that the genotype did not increase the symptoms of the disease with the improvement of the environment for disease. The variance of the regression deviations should be the smallest possible, close to zero, indicating that the cultivar modifies with the environmental variations in a predictable way, that is, following a perfect forecast line. With σ2 di high, the behavior of the genotype will be unpredictable. If β1 = 1 the genotype will be responsive to environmental improvement for disease, but in this case, for disease symptoms this is not interesting. Being β1 > 1.0 the cultivar is less responsive and less demanding, being suitable for environments of inferior quality for disease, because the diseased decrease quickly in these environments. 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(Org.); Recursos genéticos e melhoramento: plantas. Rondonópolis: Fundação MT, 2001. p. 715–735. ZHANG, Y.; XU, L.; FAN, X.; TAN, J.; CHEN, W.; XU, M. QTL mapping of resistance to gray leaf spot in maize. Theoretical and applied genetics, Berlin, v. 125, p. 1797–1808, 2012. 33 3 RESISTANCE OF CORN INBRED LINES TO FOLIAR DISEASES IN TWO PLANTING DATES 3.1 Introduction High losses in grain yield in corn are associated with the incidence of diseases. Disease monitoring studies have demonstrated that rust, gray leaf spot and Phaeosphaeria leaf spot are among the major diseases that affect the corn crop in Brazil (CARSON, 2005; CASELA et al., 2006). Due to the characteristics of corn growing in Brazil, such as plant height, length of the planting season and economic yield and, in some cases, continuous planting of corn years, the most viable measure to control disease is use of genetic resistance. For nearly two decades chemical control was practically viable only in seed production fields (GIANASI et al.,1996). Currently crops grown with the highest level of technology, with higher income potential, can often economically use chemical control for these diseases, however, the use of genetic resistance is preferred. For the farmer, the desired resistance is in the hybrid planted, but breeders must also worry about resistance in the parental lines that give rise to these hybrids. In addition, resistant inbred lines, can be used for adding resistance to other inbred lines and better performance in future hybrids. There is also the possibility of using synthetics from resistant inbreds as commercial varieties for corn producers with lower technological level. Many reports in the literature indicate that there is genetic variability in cultivars in disease resistance (NIHEI; FERREIRA, 2012; VIEIRA et al., 2012; ZAMBRANO et al., 2014); however, few papers discuss genetic resistance to diseases in inbred lines. Colombo et al. (2014) reported that the Area Under the Disease Progress Curve (AUDPC) can quantify the progression of disease during a certain period, and it has been frequently used to evaluate the level of resistance in field conditions. The objectives of this study were to identify inbred lines resistant to tropical rust (Physopella zeae (Mains) Cummins & Ramachar.), southern rust (Puccinia polysora Underw), gray leaf spot (Cercospora zeae-maydis Tehon & E.Y. Daniels), northern leaf blight (Exserohilum turcicum (Pass.) Leonard & Suggs), physoderma brown spot (Physoderma maydis) and phaeosphaeria leaf spot (Phaeosphaeria maydis in association with Pantoeae ananas), using the area under disease progress curve, in two planting dates. Evaluate resistant inbred lines which may use for obtaining synthetics and determinate the better planting date for disease resistance evaluations. 34 3.2 Material and methods Fifty inbred lines were used, eighteen derived from the Isanão-VF1 population, nine from the Isanão-VD1 population, ten from the Flintisa population, eight from the Dentado population and five from EMPASC 151- Condá. The first two populations are brachytic, the others have normal height. Flintisa and Dentado lines were obtained from the corn breeding program of São Paulo State University – UNESP – Ilha Solteira – SP (Brazil). EMPASC 151- Condá is an old open pollinated variety from the state of Santa Catarina (Brazil). The experiments were conducted at the Fazenda de Ensino e Pesquisa da UNESP - Ilha Solteira, located in Selvíria – Mato Grosso do Sul (MS) - Brazil (20° 20'S, 51° 23' and the altitude of 335 m). The climate of the region, according to Köppen classification, is Aw, defined as tropical humid with a rainy season in summer and dry in winter. The average annual rainfall is 1,330 mm, with the average annual air temperature of about 25°C and average humidity of 66% (CENTURION, 1982). The fifty experimental inbred lines were evaluated in a randomized block design with three replications in two seasons (planting on Feb,20,2014 and Apr,17,2014). Each plot was a single row 8 m in length with spacing of 0.45 m between plots and an average of 0.4 m between plants. Planting was with normal tillage, irrigated by center pivot, with twice the number of seeds needed and thinned at six fully developed leaves. Fertilization was done according to soil analysis with 300 kg ha-1 of 8-28-16 applied followed by 250 kg ha-1 of urea at the 6 leaf stage. The inbred lines were evaluated for tropical rust (TR), southern rust (SR), gray leaf spot (GLS), northern leaf blight (NLB), physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS). Evaluations were carried out at 45, 60, 75 and 90 days after planting, determining the severity of disease based on the percentage of symptoms of the plot, according to the diagrammatic scale suggested in Agroceres Guide to Sanity (AGROCERES, 1996). The ratings were assigned values of 1, 2, 3, 4, 5, 6, 7, 8 and 9, corresponding to 0, 1, 10, 20, 30, 40, 60, 80 and> 80% of leaf symptoms, respectively. These inbred lines have already been selected for good yielding in crosses. The Area Under the Disease Progress Curve (AUDPC) for each disease was calculated as suggested by Campbell and Maddenn (1990): 𝐴𝑈𝐷𝑃𝐶 = ∑(𝑌𝑖+1 + 𝑌𝑖)(𝑇𝑖+1 − 𝑇𝑖) 𝑛−1 𝑖=1 (2) 35 Where: Yi: severity of the disease at the stage of evaluation i (i = 1,...n). Yi+1: severity of the disease at the stage of evaluation i+1. Ti: evaluation stage i, is the number of days after planting. Ti+1: evaluation stage i+1. n: total number of evaluations. For statistical analysis, the scores were transformed by √𝑥 + 0.5, using the Genes software (CRUZ, 2013) for the individual analyses and the combined analyses of variance, Microsoft Excel 2010® was used for calculating the AUDPC. Temperature and humidity were collected from the weather station located near the experiment (latitude: 20º 25' 24.4" and longitude: 51º 21' 13.1") for the period from February until July 2014 (Figure 2). Source: Canal Clima UNESP (2014). 3.3 Results and discussion In the joint analyses of variance for AUDPC (Table 1), the F test for inbred line variation is significant for tropical rust (TR), southern rust (SR), gray leaf spot (GLS) and phaeosphaeria lead spot (PLS), showing that the inbred lines had different responses to the natural infection Figure 2 - Temperature and relative humidity in Ilha Solteira – SP, Brazil from February to July 2014. 36 of these diseases. However, the discrimination among inbred lines observed in the joint analysis occurred for both planting seasons only for SR. The analyses of individual seasons (Table 2) indicated that the significance of the F test for inbred lines for TR and GLS occurred only for the first season, while for the PLS significance differences occurred only for the second season. Therefore, early planting can be used to select more resistant inbred lines for TR, SR and GLS while a later planting is more appropriate for PLS. Table 1 - Joint analysis (mean squares) of Area Under the Disease Progress Curve (AUDPC) for tropical rust (TR), southern rust (SR), gray leaf spot (GLS), northern leaf blight (NLB), physoderma brown spot (PBS) and phaeosphaeria leaf spot (PLS). Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. Source of variation DF TR SR GLS NLB PBS PLS Inbred lines (L) 49 0.4665** 1.8852** 1.0177** 0.3414 0.0590 0.3511** Seasons (S) 1 0.1611 22.2522* 0.3628 0.0758 8.2220** 2.0415* Lx S 49 0.5005* 1.7671** 0.7771 0.2426 0.0545 0.2598 Error 196 0.2450 0.7678 0.5911 0.2618 0.0800 0.1999 Average 103.0 134.4 108.1 95.3 93.5 94.4 CV% 4.87 7.58 7.4 5.24 2.92 4.6 Nota: **. * Significant at 1% and 5% probability level for the F test. Source: Prepared by author For northern leaf blight (NLB) and physoderma brown spot (PBS) there was no discrimination among inbred lines in either season (Tables 1, 2, 3 and 4), with AUDPC average of 95.3 and 94.4, respectively, showing moderate resistance, inadequate conditions for the development of diseases or insufficient natural inoculum pressure. The possibility exists that the tested inbred lines are similar in levels of resistance to these two diseases. White (1999) suggests that NLB epidemics are related to temperatures around 20°C and relative humidity above 90%. These conditions of humidity were not observed in the two evaluation periods of this study. For the development of PBS, the optimum temperature is between 23°C and 30°C with constant water accumulation on the leaves and is favored by the presence of free water on the surface of leaves as described by Robertson et al. (2014), which was often observed in this study, showing weather conditions sufficient for the development of the pathogen in the tested inbred lines. As there were no differences between inbred lines, it can be considered that they have equal levels of resistance, although further assessment covering other planting dates are recommended for a more accurate conclusion on the subject. The use of a known susceptible check may be useful for this purpose, but in this work there was no prior information available 37 to select a susceptible check, as this is the first report on inbred lines from São Paulo for resistance to these diseases. Table 2 - Individual analysis of variance for Area Under the Disease Progress Curve (AUDPC) for both planting dates (season 1: 02.20.2014 and season 2: 04.17.2014) to tropical rust, southern rust, gray leaf spot, northern leaf blight, physoderma brown spot and phaeosphaeria leaf spot. Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. Source of variation DF Season 1 Season 2 Tropical rust Blocks 2 1.1953 1.0820 Inbred lines 49 0.7426** 0.2243 Error 98 0.2610 0.2290 Average - 102.65 103.4 CV% - 5.04 4.70 Southern rust Blocks 2 5.3528 0.1828 Inbred lines 49 2.2758** 1.3764* Error 98 0.6445 0.8912 Average - 140.85 128.05 CV% - 6.78 8.36 Gray leaf spot Blocks 2 0.7130 0.0427 Inbred lines 49 1.2002* 0.5946 Error 98 0.7427 0.4396 Average - 109.05 107.2 CV% - 8.27 6.40 Northern leaf blight Blocks 2 1.5406 0.9401 Inbred lines 49 0.4039 0.1801 Error 98 0.3689 0.1547 Average - 95.05 95.45 CV% - 6.22 4.01 Physoderma brown spot Blocks 2 0.0234 0.2258 Inbred lines 49 0.0119 0.1015 Error 98 0.0119 0.0893 Average - 90.25 96.75 CV% - 1.14 3.03 Phaeosphaeria leaf spot Blocks 2 0.1257 0.2984 Inbred lines 49 0.2053 0.4056* Error 98 0.1445 0.2552 Average - 92.7 96.06 CV% - 3.94 5.15 Nota **. * Significant at 1% and 5% probability level for the F test. Source: Prepared by author For seasons, significant differences were observed for SR, PBS and PLS (Table 1), with the highest incidence of the first two with planting in February and the highest incidence of PLS with planting in April (Tables 3 and 4). According to the analysis of temperature and humidity (Figure 2), it is possible to verify that in the period from February until May, which 38 includes all the evaluations of the first season, temperature and humidity were higher, while during the second season peaks of higher temperature and humidity occurred on some evaluation dates. These conditions benefited the development of SR and PBS, hindered the development of PLS and were not sufficient to alter the symptoms of other diseases. For selection, only seasons when there is discrimination of genotypes are consequential. Favorable environmental conditions can be sufficient for the development of disease epidemics, provided that sufficient inoculum exists (FERNANDES; OLIVEIRA, 2000; ROLIM et al., 2007). Figure 3 - Evolution of the scoring of the IVD1-3, IVF1-7 and IVF1-8 inbred lines for gray leaf spot in the first planting season, Selvíria - Mato Grosso do Sul (MS), Brazil, 2014. Source: Prepared by author Although it is possible select resistant genotypes when there is statistical discrimination, an issue to be discussed is what is the AUDPC limiting value to consider a genotype resistant to foliar diseases. By the Agroceres Guide to Sanity (AGROCERES, 1996), a genotype is considered resistant with score lowers than or equal to 3 at 30 days after silking. Projecting this for our evaluation dates, it would correspond to a score of 1 for 45 and 60 days, a score of 2 to 75 days and a score of 3 for 90 days. These values correspond to an AUDPC of 120, which can be regarded as a limit for a genotype to be considered resistant. Genotypes do not exhibit these 39 scores exactly, but those with AUDPC less than 120 can be considered resistant. Taking as an example GLS, the IVF1-8 inbred line (Figure 3) is highly resistant, with AUDPC equal to 90, which indicates the absence of disease symptoms. The IVD1-3 inbred line is at the resistance threshold, with AUDPC equal to 120, while the IVF1-7 inbred line is considered susceptible (Figure 3). In commercial hybrids, thinking of the farmer’s situation, this threshold could even be increased slightly, but in the selection of inbred lines for production of breeding proposes it is understood that accuracy should be stricter. For NLB, PBS and PLS there was either not much disease or almost complete resistance. Later planting had slightly increased disease scores, but artificial inoculation may be necessary to discriminate among lines. GLS and both rests had a good range of disease scores, but only the early planting had a fully susceptible line (IVF1-7) for GLS. Both rests have a wide range of scores, but these was a general lock of resistance for SR and for more infection with the earlier planting (mean of 141 vs 128). While the mean scores for TR differed little between planting dates, only one line, 1F, was highly susceptible and that was only for the early planting. Overall, where discrimination among lines was possible, the earlier planting was most useful. Analysis of the average cluster, the Scott-Knott test (Table 3), showed that there are inbred lines with different resistance levels for various diseases during the first season. In this context, the inbred lines that showed higher levels of resistance to tropical rust, southern rust, gray leaf spot, physoderma brown spot and phaeosphaeria leaf spot were: IVF1-3, IVF1-9, IVF1-10, IVF1-11, IVF1-25 and IVF1-230 from the Isanão-VF1 population; IVD1-2-1 and IVD1-12 from the Isanão-VD1 population; 2F, 3F and 6F from Flintisa population and the inbred line 4C from the Condá population. The inbred lines coming from the Isanão-VF1 had a higher frequency of inbred lines resistant to these diseases. The 1F, 5C and 9D inbred lines were the most susceptible to tropical rust, southern rust and gray leaf spot, respectively, and can be used as checks in future experiments of genotypes for evaluations of resistance to these diseases. The analysis of the average cluster, the Scott-Knott test for the second season (Table 4) was significant only for phaeosphaeria leaf spot. The inbred lines with higher values of AUDPC for phaeosphaeria were 4C, 10F, 6F, 4F, 8D, 2D, IVD1-10, IVD1-3, IVD1-2, IVF1-12-1, IVF1- 11 and IVF1-6-3. Only inbreds IVF1-11, 6F and 4C showed favorable AUDPC for the first season, while in the second season they had higher AUDPC. The analysis of the effect of seasons on phaeosphaeria leaf spot (Table 1) revealed that there were differences between the seasons, which is related to the fact that weather conditions were different in the two seasons. However, in this case, the effect of the seasons was essentially the same for all the inbred lines, 40 as evidenced by no significant interaction of season x inbred lines. The results of this study suggest the need for further assessment, in other months of planting, for the correct evaluation of symptoms of northern leaf blight and physoderma brown spot. Table 3 - Averages of inbred lines in season 1 (planting date 02.20.2014) for Area Under the Disease Progress Curve (AUDPC, non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. (To be continued) Inbred line Tropical rust Southern rust Gray leaf spot Northern leaf blight Physoderma brown spot Phaeosphaeria leaf spot IVF1-2-1 102.5 a 147.5 b 97.5 a 90 a 90 a 90 a IVF1-3 97.5 a 132.5 a 135 b 112.5 a 90 a 90 a IVF1-4 112.5 b 125 a 127.5 b 92.5 a 90 a 90 a IVF1-5 105 a 125 a 112.5 b 90 a 90 a 90 a IVF1-6-1 100 a 157.5 b 92.5 a 102.5 a 90 a 90 a IVF1-6-2 102.5 a 157.5 b 117.5 b 90 a 90 a 90 a IVF1-6-3 97.5 a 140 b 110 a 102.5 a 90 a 90 a IVF1-7 92.5 a 100 a 150 b 90 a 90 a 90 a IVF1-8 102.5 a 145 b 90 a 102.5 a 90 a 90 a IVF1-9 92.5 a 122.5 a 90 a 90 a 90 a 90 a IVF1-10 92.5 a 120 a 105 a 115 a 90 a 95 a IVF1-11 97.5 a 122.5 a 97.5 a 90 a 90 a 90 a IVF1-12 127.5 c 147.5 b 122.5 b 90 a 90 a 90 a IVF1-12-1 102.5 a 160 b 97.5 a 90 a 90 a 92.5 a IVD1-2 117.5 b 167.5 c 95 a 97.5 a 90 a 90 a IVD1-3 90 a 112.5 a 120 b 90 a 90 a 90 a IVD1-5 90 a 175 c 127.5 b 90 a 90 a 90 a IVD1-8 127.5 c 147.5 b 125 b 90 a 90 a 95 a IVD1-9 100 a 120 a 127.5 b 90 a 90 a 90 a IVD1-10 105 a 197.5 c 115 b 90 a 90 a 90 a IVD1-11 120 b 150 b 102.5 a 90 a 90 a 90 a IVD1-2-1 100 a 132.5 a 102.5 a 107.5 a 90 a 90 a IVD1-12 105 a 137.5 a 97.5 a 90 a 90 a 92.5 a 1D 110 b 147.5 b 90 a 90 a 90 a 90 a 2D 105 a 127.5 a 115 b 110 a 90 a 95 a 3D 92.5 a 152.5 b 110 a 90 a 90 a 92.5 a 6D 110 b 150 b 97.5 a 97.5 a 90 a 90 a 7D 115 b 145 b 92.5 a 90 a 95 b 112.5 b 8D 95 a 132.5 a 127.5 b 92.5 a 97.5 b 110 b 9D 92.5 a 127.5 a 125 b 90 a 90 a 90 a 10D 105 a 115 a 105 a 90 a 90 a 100 b 1F 140 c 147.5 b 117.5 b 90 a 90 a 90 a 2F 90 a 107.5 a 107.5 a 90 a 90 a 90 a 3F 97.5 a 125 a 100 a 95 a 90 a 90 a 4F 100 a 142.5 b 115 b 92.5 a 90 a 102.5 b 5F 97.5 a 187.5 c 110 a 90 a 90 a 90 a 6F 105 a 135 a 92.5 a 90 a 90 a 90 a 7F 90 a 115 a 125 b 97.5 a 90 a 92.5 a 8F 102.5 a 147.5 b 105 a 90 a 90 a 90 a 9F 97.5 a 142.5 b 97.5 a 105 a 90 a 100 b 41 Table 4 - Averages of inbred lines in season 1 (planting date 02.20.2014) for Area Under the Disease Progress Curve (AUDPC, non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. (Conclusion) 10F 107.5 a 175 c 122.5 b 90 a 90 a 90 a IVF1-5-2 95 a 152.5 b 112.5 a 102.5 a 90 a 110 b IVF1-247 110 b 130 a 110 a 112.5 a 90 a 95 a IVF1-25 105 a 122.5 a 105 a 90 a 90 a 92.5 a IVF1-230 92.5 a 120 a 90 a 90 a 90 a 90 a 1C 107.5 a 165 c 117.5 b 90 a 90 a 90 a 2C 92.5 a 147.5 b 102.5 a 90 a 90 a 90 a 3C 95 a 142.5 b 105 a 102.5 a 90 a 90 a 4C 100 a 117.5 a 100 a 115 a 90 a 92.5 a 5C 102.5 a 180 c 97.5 a 97.5 a 90 a 95 a Average 102.65 140.85 109.05 95.05 90.25 92.7 * - Average with the same letter do not differ by the Scott-Knott test at 5% probability. Source: Prepared by author Table 5 - Averages of inbred line in season 2 (planting date 04.17.2014) for Area Under the Disease Progress Curve (AUDPC non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. (To be continued) Inbred line Tropical rust Southern rust Gray leaf spot Northern leaf blight Physoderma brown spot Phaeosphaeria leaf spot IVF1-2-1 115 a 127.5 a 97.5 a 107.5 a 95 a 90 a IVF1-3 102.5 a 130 a 132.5 a 95 a 92.5 a 97.5 a IVF1-4 107.5 a 142.5 a 102.5 a 97.5 a 95 a 92.5 a IVF1-5 95 a 117.5 a 112.5 a 92.5 a 97.5 a 92.5 a IVF1-6-1 97.5 a 105 a 122.5 a 95 a 97.5 a 90 a IVF1-6-2 105 a 120 a 110 a 95 a 102.5 a 92.5 a IVF1-6-3 115 a 155 a 102.5 a 90 a 102.5 a 102.5 b IVF1-7 102.5 a 110 a 105 a 95 a 102.5 a 95 a IVF1-8 105 a 130 a 97.5 a 97.5 a 95 a 90 a IVF1-9 102.5 a 127.5 a 107.5 a 92.5 a 95 a 97.5 a IVF1-10 102.5 a 147.5 a 95 a 90 a 92.5 a 90 a IVF1-11 95 a 152.5 a 97.5 a 92.5 a 97.5 a 115 b IVF1-12 97.5 a 112.5 a 105 a 95 a 97.5 a 92.5 a IVF1-12-1 97.5 a 142.5 a 115 a 90 a 95 a 115 b IVD1-2 100 a 132.5 a 120 a 92.5 a 102.5 a 105 b IVD1-3 112.5 a 145 a 140 a 95 a 95 a 105 b IVD1-5 100 a 107.5 a 115 a 97.5 a 100 a 97.5 a IVD1-8 110 a 110 a 102.5 a 100 a 97.5 a 92.5 a IVD1-9 102.5 a 127.5 a 115 a 92.5 a 97.5 a 90 a IVD1-10 115 a 132.5 a 100 a 92.5 a 92.5 a 107.5 b IVD1-11 97.5 a 122.5 a 107.5 a 92.5 a 95 a 92.5 a IVD1-2-1 100 a 132.5 a 125 a 90 a 95 a 90 a IVD1-12 112.5 a 142.5 a 100 a 97.5 a 95 a 92.5 a 1D 95 a 145 a 107.5 a 97.5 a 95 a 90 a 2D 102.5 a 107.5 a 102.5 a 112.5 a 92.5 a 102.5 b 3D 102.5 a 130 a 97.5 a 90 a 97.5 a 90 a 6D 107.5 a 120 a 102.5 a 90 a 105 a 90 a 7D 102.5 a 120 a 97.5 a 97.5 a 95 a 95 a 8D 97.5 a 147.5 a 100 a 92.5 a 100 a 115 b 42 Table 6 - Averages of inbred line in season 2 (planting date 04.17.2014) for Area Under the Disease Progress Curve (AUDPC non-transformed). Selvíria - Mato Grosso do Sul (MS), Brazil. 2014. (Conclusion) 9D 107.5 a 112.5 a 97.5 a 90 a 97.5 a 95 a 10D 100 a 107.5 a 105 a 95 a 90 a 92.5 a 1F 102.5 a 145 a 115 a 92.5 a 105 a 90 a 2F 110 a 130 a 100 a 95 a 92.5 a 90 a 3F 95 a 150 a 107.5 a 92.5 a 97.5 a 90 a 4F 107.5 a 112.5 a 125 a 92.5 a 97.5 a 105 b 5F 107.5 a 130 a 100 a 92.5 a 95 a 92.5 a 6F 107.5 a 152.5 a 105 a 107.5 a 95 a 115 b 7F 102.5 a 107.5 a 100 a 90 a 95 a 90 a 8F 102.5 a 120 a 102.5 a 92.5 a 95 a 90 a 9F 110 a 102.5 a 102.5 a 95 a 92.5 a 92.5 a 10F 112.5 a 122.5 a 97.5 a 95 a 97.5 a 102.5 b IVF1-5-2 105 a 142.5 a 110 a 97.5 a 90 a 92.5 a IVF1-247 107.5 a 115 a 107.5 a 107.5 a 95 a 97.5 a IVF1-25 97.5 a 127.5 a 102.5 a 100 a 92.5 a 90 a IVF1-230 97.5 a 130 a 112.5 a 92.5 a 102.5 a 95 a 1C 102.5 a 125 a 115 a 100 a 97.5 a 90 a 2C 105 a 165 a 102.5 a 97.5 a 100 a 90 a 3C 97.5 a 120 a 112.5 a 92.5 a 100 a 92.5 a 4C 97.5 a 105 a 105 a 102.5 a 95 a 107.5 b 5C 97.5 a 137.5 a 100 a 97.5 a 102.5 a 95 a Average 103.4 128.05 107.2 95.45 96.75 96.05 * - Average with the same letter do not differ by the Scott-Knott test at 5% probability Source: Prepared by author 3.4 Conclusion The resistant inbred lines based on Area Under Disease Progress Curve (AUDPC) for southern rust, tropical rust, gray leaf spot, northern leaf blight, phaeosphaeria leaf spot and physoderma brown spot were IVF1-3, IVF1-9, IVF1-10, IVF1-11, IVF1-25, IVF1-230, IVD1- 2-1, IVD1-12, 2F, 3F, 6F and 4C. The inbred lines with dent grains, are the most susceptible, the condá and dentado population have low frequency of alleles of resistance to studied diseases. The inbred lines from flint grains, isanão-VF1 and Flintisa population have higher frequency of alleles for most diseases resistance evaluated when compared to other populations. The results of this study suggest the need for further assessment, in other months of planting times to determine the best period for disease incidence and discrimination among genotypes for northern leaf blight and physoderma brown spot. 43 REFERENCES AGROCERES. Guia de sanidade agroceres. São Paulo: Sementes agroceres, 1996.72 p. CAMPBELL, C. L.; MADDENN, L. V. Introduction to plant disease epidemiology. New York: Wiley, 1990. 532 p. CARSON, M. L. Yield loss potential of phaeosphaeria leaf spot of maize caused by Phaeosphaeria maydis in the United States. Plant disease, Saint Paul, v. 89, n. 9, p. 986–988, 2005. CASELA, C. R.; FERREIRA, A. S.; PINTO, N. F. J. A. Doenças na cultura do milho. Circular técnica 83: Embrapa, Sete Lagoas, v. 83, p. 1–14, 2006. CENTURION, J. F. Balanço hídrico da região de Ilha Solteira. Científica, Jaboticabal, v. 10, n. 1, p. 57–61, 1982. COLOMBO, G. A.; VAZ-DE-MELO, A.; TAUBINGER, M.; TAVARES, R. D. C. Análise dialélica para resistência a ferrugem polissora em milho em diferentes níveis de adubação fosfatada. Bragantia, Campinas, v. 73, n. 1, p. 65–71, 2014. CRUZ, C. D. GENES - a software package for analysis in experimental statistics and quantitative genetics. Acta scientiarum agronomy, Maringá, v. 35, n. 3, p. 271–276, 2013. FERNANDES, F. T.; OLIVEIRA, E. Principais doenças na cultura do milho. Sete Lagoas: Embrapa - CNPMS, 2000. 80 p. GIANASI, L.; CASTRO, H. A.; SILVA, H. P. Raças fisiológicas de Exserohilum turcicum identificados em regiões produtoras de milho no Brasil, safra 93/94. Summa phytopathologica, Botucatu, v. 22, p. 214–217, 1996. NIHEI, T. H.; FERREIRA, J. M. Análise dialélica de linhagens de milho com ênfase na resistência a doenças foliares. Pesquisa agropecuaria brasileira, Brasília, DF, v. 47, n. 3, p. 369–377, 2012. ROBERTSON, A. E.; MUELLER, D. S.; SAALAU ROJAS, E.; MUNKVOLD, G. P. Stalk breakage and rot caused by physoderma in Iowa. Ames: Integrated crop management News, 2014. Paper 20. Disponível em: . Acesso em: 5 dez. 2014. ROLIM, G. S.; PEDRO JÚNIOR, M. J.; FANTIN, G. M.; BRUNINI, O.; DUARTE, A.P.; DUDIENAS, C. Modelo agrometeorológico regional para estimativa da severidade da mancha de phaeosphaeria em milho safrinha no Estado de São Paulo, Brasil. Bragantia, Campinas, v. 66, n. 4, p. 721–728, 2007. UNIVERSIDADE ESTADUAL PAULISTA – UNESP. Faculdade de Engenharia. Canal Clima da Unesp. Ilha Solteira, 2014. Disponível em: . Acesso em: 10 nov. 2014. 44 VIEIRA, R. A.; SCAPIM, C. A.; MOTERLE, L. M.; TESSMANN, D. J.; AMARAL JUNIOR, A. T. do; GONÇALVES, L. S. A.; The breeding possibilities and genetic parameters of maize resistance to foliar diseases. Euphytica, Wageningen, v. 185, n. 3, p. 325–336, 2012. WHITE, D. G. Compendium of corn diseases. 3. ed. Saint Paul: American phytopathological society, 1999. ZAMBRANO, J. L.; JONES, M. W.; BRENNER, E.; FRANCIS, D. M.; TOMAS, A.; REDINBAUGH, M. G. Genetic analysis of resistance to six virus diseases in a multiple virus- resistant maize inbred line. Theoretical and applied genetics, Berlin, v. 127, n. 4, p. 867–80, 2014. 45 4. ADAPTABILITY AND STABILITY OF CORN INBRED LINES FOR RESISTANCE TO GRAY LEAF SPOT AND NORTHERN LEAF BLIGHT 4.1 Introduction Gray leaf spot (Cercospora zeae-maydis Tehon & E.Y. Daniels) and northern leaf blight (Exserohilum turcicum (Pass.) Leonard & Suggs) are among the foliar diseases that affect the corn crop in Brazil and worldwide. Susceptible genotypes to these diseases are responsible for causing severe losses in grain yield since they result directly in decreased photosynthetic area due to the destruction of the green tissues. A 50% reduction the catchment of incident radiation caused by the decrease in green tissue 15 days before and after female flowering may represent a reduction of 40% to 50% of grain yield (FISCHER; PALMER, 1984). Gray leaf spot was first described in the corn crop in Illinois, United States, in 1925. In Brazil, it was described by Chupp (1953), but the disease becomes common in 2000, when epidemy were reported in production fields in the central region of the country, due to the increase of inoculum promoted by the cultural tillage, irrigation pivot and planting the second season (BRITO et al., 2007). The symptoms of gray leaf spot appear first on lower leaves, about two or three weeks before tasseling, leaf lesions are long, with a rectangular shape, and elliptical. Leaf lesions are brown and with high humidity conditions (above 90%), daytime temperatures ranging from moderate to high (22º to 32º C) and cold nights with dew, occurs dense sporulation, rendering the leaves in gray, characteristic of this disease (CASELA; FERREIRA, 2003; ROBERTSON et al., 2008). Brito et al. (2007) evaluating 12 commercial corn hybrids for the incidence of gray leaf spot, showed that the level of damage caused by the pathogen change between planting dates and hybrids. The reduction in grain yield is mainly related to the late planting date and the use of resistant hybrids dispenses the chemical control. Silva et al. (2012) evaluating two transgenic corn hybrids in two populations (78.000 and 100.000 plants per hectare) concluded that the lower density of plants favored the increase in the severity of disease and contributing to decrease in grain yield. Northern leaf blight is distributed worldwide and can cause yield losses of more than 60% in susceptible germplasm (RAYMUNDO; HOOKER, 1981). The disease symptoms appear about a week after beginning of infection, characterized by presenting elliptical straw lesions measuring 2.5 to 15 cm in length with well defined edges, which become dark because of fungus fructification (WORDELL FILHO; CASA, 2012). The development of northern leaf 46 blight is favored by a temperature between 18º and 27ºC with an optimum temperature of 20ºC and presence of dew on the leaf surface (SABATO et al., 2013). In Brazil, the disease occurs strongly in the second season due to the most damage when it infects the plants in the female flowering period. According to Fernandes and Oliveira (2000), the development of E. turcicum is negatively correlated with the photoperiod, light intensity and the concentration of sugar in corn. These conditions are most often seen in the second season crops, which could explain the higher severity of this pathogen at the time. Many authors describe the mechanisms of inheritance associated with northern leaf blight. The disease is controlled mainly using resistant cultivars by quantitative resistance (non- race-specific) and qualitative (race-specific). Qualitative and quantitative sources of resistance have been described (WELZ; GEIGER, 2000). The quantitative resistance conferring partial resistance in the northern leaf blight causes reduction in the development of the disease and the percentage of affected leaf area, which may affect of the epidemics, including the incubation period, latent period, intensity of sporulation, the size, number and growth rate of lesions (PARLEVLIET, 2002; CARSON; GOODMAN, 2006, HURNI et al., 2015). The interaction between the host and pathogen is distinct in different environments; it is often possible to observe a significant interaction between genotype and environment, which may cause variation in disease severity due to the instability of resistance loci in the interaction with the environment or differences in pathogen populations between environments (CARSON et al., 2002). In this context, the objectives of this study were to identify resistant and susceptible inbred lines based on stability and adaptability for disease symptoms to gray leaf spot and northern leaf blight, suggest resistant inbred lines aimed at producing synthetics, as well as identify the planting dates with the higher occurrence of these two diseases to use them for genetic resistance identification. 4.2 Material and methods Forty-one inbred lines were used, fourteen derived from the Isanão-VF1 population, nine from the Isanão-VD1 population, ten from the Flintisa population and eight from the Dentado population. The first two populations are brachytic, with flint and dent grains, respectively. The others have normal height, also with flint and dent grains. The inbred lines were obtained from the corn breeding program of São Paulo State University (UNESP) – Campus of Ilha Solteira – SP (Brazil), and have already been selected for general combining ability for yield. 47 The experiments were conducted at the Fazenda de Ensino e Pesquisa da UNESP – Campus of Ilha Solteira, located in Selvíria – Mato Grosso do Sul (MS) - Brazil (20° 20'S, 51° 23'W and the altitude of 335 m). The climate of the region, according to Köppen classification, is Aw, defined as tropical humid with a rainy season in summer and dry in winter. The average annual rainfall is 1330 mm, with the average annual air temperature of about 25°C and average humidity of 66% (CENTURION, 1982). Forty-one experimental inbred lines were evaluated in a randomized block design with three replications in eleven planting dates (October and November 2013 and January until September 2014), with each planting being considered as an environment. Each plot was a single row 8 m in length with a spacing of 0.45 m between plots and an average of 0.4 m between plants. Planting was with normal tillage, irrigated by a center pivot, with twice the number of seeds needed and thinned at six fully developed leaves. Fertilization was done according to soil analysis with 300 kg ha-1 of 8-28-16 applied followed by 250 kg ha-1 of urea sidedress at the six-leaf stage. Temperature and relative humidity were collected from the weather station located near the experiment during all growing seasons (Figure 4). Figure 4 - Temperature, rain and humidity in Selvíria - MS, Brazil from October 2013 until December 2014. Source: Prepared by author The inbred lines were evaluated for gray leaf spot (GLS) and northern leaf blight (NLB). Evaluations were carried out at 30 days after silking, determining the severity of disease based 0 50 100 150 200 0 20 40 60 80 100 Temperature ºC Humidity % Rain mm 48 on the percentage of symptoms of the plot, according to the diagrammatic scale suggested in the Agroceres Guide to Sanity (AGROCERES, 1996). The ratings were assigned values of 1, 2, 3, 4, 5, 6, 7, 8 and 9, corresponding to 0, 1, 10, 20, 30, 40, 60, 80 and> 80% of leaf symptoms, respectively for each plant plot, using the average plot for statistical analysis. The scores were further classified into the following disease reaction types: 1 – highly resistant; 2-3 – resistant; 4 – moderately resistant; 5 – moderately resistant/moderately susceptible; 6 – moderately susceptible; 7-8 – susceptible and 9- highly susceptible. The original scores were transformed by √𝑥 + 0.5, and a joint analysis was performed, considering each month as a season of planting and inbred lines with fixed effects and environments as random effects. The Hartley test, which is based on the ratio between the largest and smallest error mean square, was employed, considering the ratio higher than seven as an indication that the error variances were not homogeneous (PIMENTEL GOMES, 2000). To assure the homogeneity of residual variance, the degrees of freedom from residue and inbred lines x environment interaction were adjusted as recommended by Cochran (1954). For adaptability and stability analysis, the following model, based on regression (Eberhart; Russell, 1966) was used: 𝑌𝑖𝑗 = 𝛽𝑜𝑖 + 𝛽1𝑖𝐼𝑗 + 𝛿𝑖𝑗 + 휀𝑖𝑗 (3) Where: 𝛽𝑜𝑖: overall average of genotype i; 𝛽1𝑖: linear response of genotype i for environmental variation; 𝐼𝑗: environmental index (j = 1, 2, …, a), being 𝐼𝑗 = 𝑌.𝑗 𝑔 − 𝑌 𝑔𝑎 ; 𝛿𝑖𝑗: deviation from regression 휀𝑖𝑗: experimental error. Data analysis was performed using the Genes Software, version 2015.5.0 (CRUZ, 2013). 49 4.3 Results and discussion The F test of joint variance analysis for GLS was significant for inbred lines (L), environments (E) and LxE interactions (P<0.01), while for NLB, the F test, was significant (P<0.01) for environments and LxE interactions (Table 5). As the LxE was significant for each disease studied, were performed the adaptability and stability analysis proposed by Eberhart and Russell (1966). Table 7 - Summary of the joint variance analysis for Gray leaf spot score (GLS) and Northern leaf blight score (NLB), for 41 corn inbred lines in 11 environments. Selvíria – MS, Brazil, 2014. Source of variation DF GLS DF NLB Inbred lines (L) 40 0.267** 40 0.0723 Environments (E) 10 9.900** 10 2.240** Lx E 333 0.134** 254 0.084** Error 720 0.568 538 0.059 Average 2.2 1.7 CV% 14.78 16.67 Note. ** - Significant by the F test (p ≤ 0.01). Source: Prepared by author The severity values for GLS and NLB were different in different months of planting. Averages and environmental indexes (Ij) for each month (Table 6) showed that in the 2013 planting dates and January, February, March and May 2014, there was less GLS pressure and, for NLB the lowest averages were observed in October 2013, January, February and March 2014. In the planting dates of August and September 2014, the highest average scores of inbred lines to the severity of both diseases were found. According to the averages (β0), adaptability parameters (β1) and phenotypic stability (𝒅𝒊 𝟐 ) (Table 7) for severity of GLS, 7F, 9F and IVF1-7 presented β1<1. The inbred lines IVF1- 6-1, IVF1-6-2, IVF1-12, IVD1-2-1, IVD1-5, 5F and 6F presented β1>1, and significantly increased the symptoms of the disease, with increasing of Ij. For the other inbred lines, the regression coefficient (β1) was equal to 1. Inbred lines IVD1-8 and 1F responded positively to significantly increase the Ij for NLB. The inbred line IVF1-7 with low average (1.5) for NLB and β1<1 approached the ideal for a resistant genotype. For the other inbred lines, the average ranged between 0.57 and 1.31 and the regression coefficients were equal to 1. 50 Table 8 - Environmental indexes (Ij) and environmental averages of 41 corn inbred lines in 11 environments for gray leaf spot (GLS) and northern leaf blight (NLB). Selvíria – MS, Brazil, 2014. Environment GLS NLB Average Ij Average Ij Oct-13 1.05 -0.3725 1.6 -0.0458 Nov-13 1.25 -0.3011 1.9 0.0692 Jan-14 1.34 -0.2717 1.3 -0.1356 Feb-14 1.44 -0.25 1.1 -0.2073 Mar-14 1.76 -0.1424 1.0 -0.2233 Apr-14 2.66 0.1559 1.8 0.0576 May-14 2.01 -0.0534 1.7 0.0086 Jun-14 3.14 0.262 2.0 0.1067 Jul-14 3.09 0.2593 2.0 0.0821 Aug-14 3.58 0.3791 2.1 0.1211 Sep-14 3.37 0.3348 2.2 0.1668 Source: Prepared by author. According to the phenotypic stability parameter for severity of GLS, the inbred lines IVF1-2-1, IVF1-3, IVF1-7, IVF1-8, IVF1-11, IVF1-12, IVD1-10, IVD1-11, 6D, 8D, 9D, 10D, 4F, 5F, 6F and 8F were considered unstable (𝒅𝒊 𝟐 non-zero). For the severity of NLB, all variances of the deviations were less than 0.1 and all lines considered stable. Gray leaf spot (GLS) is more severe and damaging in periods with high humidity, caused by the accumulation of water on the leaf surface and temperatures between 22º and 30ºC (BECKMAN; PAYNE, 1982). The northern leaf blight (NLB) requires temperatures between 18º and 27ºC, with the optimum at 20º C and the presence of dew on the leaf surface (SABATO et al., 2013). Throughout the period that included the first planting date to the final evaluation, the temperature conditions were favorable for the development of GLS and NLB. In the period of July to October 2014, the humidity was close to 70%, which is considered less than ideal for the development of the two diseases. Nevertheless, the severity of the GLS and NLB was largest then because the experiments were conducted under center pivot irrigation, which ensured the presence of free water in the leaves, providing the right conditions for the development of diseases. Therefore, the low averages are due to the good level of resistance of inbred lines with selection using same the standard method. Even so, small environment variations allowed verification of the best times to evaluate the resistance to both diseases are the planting dates between June and September, where Ij were positive and high (Table 6). Following the methodology proposed by Eberhart and Russell (1966), genotypes with ideal resistance would mean average scale of symptoms around 1, regression coefficient lower than 1, and no significant regression deviations. Ideally, the disease would not consistently 51 increase with the improvement of the environment for the disease (positive Ij), which occurred with inbred lines IVF1-7 and 9F for GLS and IVF1-7 for NLB. However, IVF1-7 it was unstable for GLS, as evidenced by the significance of the variance of the deviations and low coefficient of determination. Inbred lines IVF1-6-1 and IVD1-5 (B1>1) had strongly increased GLS symptoms with increasing Ij and may be considered the most susceptible group, being interesting to use only in low-pressure conditions disease (negative Ij). For NLB this occurred with IVD1-8 and 1F inbred lines. In a quantitative approach, the inheritance of resistance and the type of response to Ij depends on the concentration of alleles for disease resistance in each genotype, sensitivity of encoded product of these alleles to environmental changes and the sensitivity of regulatory factors involved in the expression of these alleles. A higher concentration of favorable alleles initially causes the genotype to resist increased disease pressure (positive Ij) and not consistently increasing the scale of symptoms even in the best condition for the disease. This effect will be maximized if the regulation of these alleles and the action mechanisms of their products are also uniformly positive, even with the change of environment. If a genotype has a good concentration of alleles for resistance, but are disabled, in control level or encoded products, their behavior will be unstable with high regression coefficient, as possibly happened with the inbred lines IVF1-6-1 and IVD1-5 for GLS and IVD1-8 and 1F for NLB. For all those unstable GLS inbred lines, one or more such effects may have also occurred. Another aspect that can give a low response to Ij and good stability is the presence of a locus with major effect, little influenced by the environment. In another case, a locus of small effect, with a